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6.895 Computational Biology: Genomes, Networks, Evolution (MIT) 6.895 Computational Biology: Genomes, Networks, Evolution (MIT)

Description

This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include:Genomes: Biological Sequence Analysis, Hidden Markov Models, Gene Finding, RNA Folding, Sequence Alignment, Genome Assembly.Networks: Gene Expression Analysis, Regulatory Motifs, Graph Algorithms, Scale-free Networks, Network Motifs, Network Evolution.Evolution: Comparative Genomics, Phylogenetics, Genome Duplication, Genome Rearrangements, Evolutionary Theory, Rapid Evolution. This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include:Genomes: Biological Sequence Analysis, Hidden Markov Models, Gene Finding, RNA Folding, Sequence Alignment, Genome Assembly.Networks: Gene Expression Analysis, Regulatory Motifs, Graph Algorithms, Scale-free Networks, Network Motifs, Network Evolution.Evolution: Comparative Genomics, Phylogenetics, Genome Duplication, Genome Rearrangements, Evolutionary Theory, Rapid Evolution.

Subjects

Genomes: Biological sequence analysis | Genomes: Biological sequence analysis | hidden Markov models | hidden Markov models | gene finding | gene finding | RNA folding | RNA folding | sequence alignment | sequence alignment | genome assembly | genome assembly | Networks: Gene expression analysis | Networks: Gene expression analysis | regulatory motifs | regulatory motifs | graph algorithms | graph algorithms | scale-free networks | scale-free networks | network motifs | network motifs | network evolution | network evolution | Evolution: Comparative genomics | Evolution: Comparative genomics | phylogenetics | phylogenetics | genome duplication | genome duplication | genome rearrangements | genome rearrangements | evolutionary theory | evolutionary theory | rapid evolution | rapid evolution

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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6.047 Computational Biology: Genomes, Networks, Evolution (MIT) 6.047 Computational Biology: Genomes, Networks, Evolution (MIT)

Description

This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include: Genomes: biological sequence analysis, hidden Markov models, gene finding, RNA folding, sequence alignment, genome assembly Networks: gene expression analysis, regulatory motifs, graph algorithms, scale-free networks, network motifs, network evolution Evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory, rapid evolution This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include: Genomes: biological sequence analysis, hidden Markov models, gene finding, RNA folding, sequence alignment, genome assembly Networks: gene expression analysis, regulatory motifs, graph algorithms, scale-free networks, network motifs, network evolution Evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory, rapid evolution

Subjects

computational biology | computational biology | algorithms | algorithms | machine learning | machine learning | biology | biology | biological datasets | biological datasets | genomics | genomics | proteomics | proteomics | genomes | genomes | sequence analysis | sequence analysis | sequence alignment | sequence alignment | genome assembly | genome assembly | network motifs | network motifs | network evolution | network evolution | graph algorithms | graph algorithms | phylogenetics | phylogenetics | comparative genomics | comparative genomics | python | python | probability | probability | statistics | statistics | entropy | entropy | information | information

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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6.096 Algorithms for Computational Biology (MIT) 6.096 Algorithms for Computational Biology (MIT)

Description

This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks. This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks.

Subjects

biological sequence analysis | biological sequence analysis | gene finding | gene finding | motif discovery | motif discovery | RNA folding | RNA folding | global and local sequence alignment | global and local sequence alignment | genome assembly | genome assembly | comparative genomics | comparative genomics | genome duplication | genome duplication | genome rearrangements | genome rearrangements | evolutionary theory | evolutionary theory | gene expression | gene expression | clustering algorithms | clustering algorithms | scale-free networks | scale-free networks | machine learning applications | machine learning applications

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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18.417 Introduction to Computational Molecular Biology (MIT) 18.417 Introduction to Computational Molecular Biology (MIT)

Description

This course introduces the basic computational methods used to understand the cell on a molecular level. It covers subjects such as the sequence alignment algorithms: dynamic programming, hashing, suffix trees, and Gibbs sampling. Furthermore, it focuses on computational approaches to: genetic and physical mapping; genome sequencing, assembly, and annotation; RNA expression and secondary structure; protein structure and folding; and molecular interactions and dynamics. This course introduces the basic computational methods used to understand the cell on a molecular level. It covers subjects such as the sequence alignment algorithms: dynamic programming, hashing, suffix trees, and Gibbs sampling. Furthermore, it focuses on computational approaches to: genetic and physical mapping; genome sequencing, assembly, and annotation; RNA expression and secondary structure; protein structure and folding; and molecular interactions and dynamics.

Subjects

basic computational methods cell on a molecular level | basic computational methods cell on a molecular level | sequence alignment algorithms | sequence alignment algorithms | dynamic programming | dynamic programming | hashing | hashing | suffix trees | suffix trees | Gibbs sampling | Gibbs sampling | genetic and physical mapping | genetic and physical mapping | genome sequencing | genome sequencing | assembly | assembly | and annotation | and annotation | RNA expression and secondary structure | RNA expression and secondary structure | protein structure and folding | protein structure and folding | and molecular interactions and dynamics | and molecular interactions and dynamics | annotation | annotation | molecular interactions and dynamics | molecular interactions and dynamics

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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HST.508 Quantitative Genomics (MIT) HST.508 Quantitative Genomics (MIT)

Description

This course provides a foundation in the following four areas: evolutionary and population genetics; comparative genomics; structural genomics and proteomics; and functional genomics and regulation. This course provides a foundation in the following four areas: evolutionary and population genetics; comparative genomics; structural genomics and proteomics; and functional genomics and regulation.

Subjects

genomics | genomics | quantitative genomics | quantitative genomics | comparative genomics | comparative genomics | genes | genes | genome | genome | SNPs | SNPs | haplotypes | haplotypes | sequence alignment | sequence alignment | protein structure | protein structure | protein folding | protein folding | proteomics | proteomics | structural genomics | structural genomics | functional genomics | functional genomics | networks | networks | systems biology | systems biology | biological networks | biological networks | RNA | RNA | DNA | DNA | gene expression | gene expression | evolutionary genetics | evolutionary genetics | population genetics | population genetics

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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7.91J Foundations of Computational and Systems Biology (MIT) 7.91J Foundations of Computational and Systems Biology (MIT)

Description

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas. This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

Subjects

7.91 | 7.91 | 20.490 | 20.490 | 20.390 | 20.390 | 7.36 | 7.36 | 6.802 | 6.802 | 6.874 | 6.874 | HST.506 | HST.506 | computational biology | computational biology | systems biology | systems biology | bioinformatics | bioinformatics | artificial intelligence | artificial intelligence | sequence analysis | sequence analysis | proteomics | proteomics | sequence alignment | sequence alignment | protein folding | protein folding | structure prediction | structure prediction | network modeling | network modeling | phylogenetics | phylogenetics | pairwise sequence comparisons | pairwise sequence comparisons | ncbi | ncbi | blast | blast | protein structure | protein structure | dynamic programming | dynamic programming | genome sequencing | genome sequencing | DNA | DNA | RNA | RNA | x-ray crystallography | x-ray crystallography | NMR | NMR | homologs | homologs | ab initio structure prediction | ab initio structure prediction | DNA microarrays | DNA microarrays | clustering | clustering | proteome | proteome | computational annotation | computational annotation

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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7.91J Foundations of Computational and Systems Biology (MIT) 7.91J Foundations of Computational and Systems Biology (MIT)

Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology. Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Subjects

computational biology | computational biology | systems biology | systems biology | bioinformatics | bioinformatics | sequence analysis | sequence analysis | proteomics | proteomics | sequence alignment | sequence alignment | protein folding | protein folding | structure prediction | structure prediction | network modeling | network modeling | phylogenetics | phylogenetics | pairwise sequence comparisons | pairwise sequence comparisons | ncbi | ncbi | blast | blast | protein structure | protein structure | dynamic programming | dynamic programming | genome sequencing | genome sequencing | DNA | DNA | RNA | RNA | x-ray crystallography | x-ray crystallography | NMR | NMR | homologs | homologs | ab initio structure prediction | ab initio structure prediction | DNA microarrays | DNA microarrays | clustering | clustering | proteome | proteome | computational annotation | computational annotation | BE.490J | BE.490J | 7.91 | 7.91 | 7.36 | 7.36 | BE.490 | BE.490 | 20.490 | 20.490

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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6.047 Computational Biology (MIT) 6.047 Computational Biology (MIT)

Description

This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets. This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

Subjects

Genomes | Genomes | Networks | Networks | Evolution | Evolution | computational biology | computational biology | genomics | genomics | comparative genomics | comparative genomics | epigenomics | epigenomics | functional genomics | motifs | functional genomics | motifs | phylogenomics | phylogenomics | personal genomics | personal genomics | algorithms | algorithms | machine learning | machine learning | biology | biology | biological datasets | biological datasets | proteomics | proteomics | sequence analysis | sequence analysis | sequence alignment | sequence alignment | genome assembly | genome assembly | network motifs | network motifs | network evolution | network evolution | graph algorithms | graph algorithms | phylogenetics | phylogenetics | python | python | probability | probability | statistics | statistics | entropy | entropy | information | information

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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7.91J Foundations of Computational and Systems Biology (MIT)

Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Subjects

computational biology | systems biology | bioinformatics | sequence analysis | proteomics | sequence alignment | protein folding | structure prediction | network modeling | phylogenetics | pairwise sequence comparisons | ncbi | blast | protein structure | dynamic programming | genome sequencing | DNA | RNA | x-ray crystallography | NMR | homologs | ab initio structure prediction | DNA microarrays | clustering | proteome | computational annotation | BE.490J | 7.91 | 7.36 | BE.490 | 20.490

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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7.91J Foundations of Computational and Systems Biology (MIT)

Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Subjects

computational biology | systems biology | bioinformatics | sequence analysis | proteomics | sequence alignment | protein folding | structure prediction | network modeling | phylogenetics | pairwise sequence comparisons | ncbi | blast | protein structure | dynamic programming | genome sequencing | DNA | RNA | x-ray crystallography | NMR | homologs | ab initio structure prediction | DNA microarrays | clustering | proteome | computational annotation | BE.490J | 7.91 | 7.36 | BE.490 | 20.490

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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7.91J Foundations of Computational and Systems Biology (MIT)

Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Subjects

computational biology | systems biology | bioinformatics | sequence analysis | proteomics | sequence alignment | protein folding | structure prediction | network modeling | phylogenetics | pairwise sequence comparisons | ncbi | blast | protein structure | dynamic programming | genome sequencing | DNA | RNA | x-ray crystallography | NMR | homologs | ab initio structure prediction | DNA microarrays | clustering | proteome | computational annotation | BE.490J | 7.91 | 7.36 | BE.490 | 20.490

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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6.895 Computational Biology: Genomes, Networks, Evolution (MIT)

Description

This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include:Genomes: Biological Sequence Analysis, Hidden Markov Models, Gene Finding, RNA Folding, Sequence Alignment, Genome Assembly.Networks: Gene Expression Analysis, Regulatory Motifs, Graph Algorithms, Scale-free Networks, Network Motifs, Network Evolution.Evolution: Comparative Genomics, Phylogenetics, Genome Duplication, Genome Rearrangements, Evolutionary Theory, Rapid Evolution.

Subjects

Genomes: Biological sequence analysis | hidden Markov models | gene finding | RNA folding | sequence alignment | genome assembly | Networks: Gene expression analysis | regulatory motifs | graph algorithms | scale-free networks | network motifs | network evolution | Evolution: Comparative genomics | phylogenetics | genome duplication | genome rearrangements | evolutionary theory | rapid evolution

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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7.91J Foundations of Computational and Systems Biology (MIT)

Description

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

Subjects

7.91 | 20.490 | 20.390 | 7.36 | 6.802 | 6.874 | HST.506 | computational biology | systems biology | bioinformatics | artificial intelligence | sequence analysis | proteomics | sequence alignment | protein folding | structure prediction | network modeling | phylogenetics | pairwise sequence comparisons | ncbi | blast | protein structure | dynamic programming | genome sequencing | DNA | RNA | x-ray crystallography | NMR | homologs | ab initio structure prediction | DNA microarrays | clustering | proteome | computational annotation

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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18.417 Introduction to Computational Molecular Biology (MIT)

Description

This course introduces the basic computational methods used to understand the cell on a molecular level. It covers subjects such as the sequence alignment algorithms: dynamic programming, hashing, suffix trees, and Gibbs sampling. Furthermore, it focuses on computational approaches to: genetic and physical mapping; genome sequencing, assembly, and annotation; RNA expression and secondary structure; protein structure and folding; and molecular interactions and dynamics.

Subjects

basic computational methods cell on a molecular level | sequence alignment algorithms | dynamic programming | hashing | suffix trees | Gibbs sampling | genetic and physical mapping | genome sequencing | assembly | and annotation | RNA expression and secondary structure | protein structure and folding | and molecular interactions and dynamics | annotation | molecular interactions and dynamics

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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6.047 Computational Biology (MIT)

Description

This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

Subjects

Genomes | Networks | Evolution | computational biology | genomics | comparative genomics | epigenomics | functional genomics | motifs | phylogenomics | personal genomics | algorithms | machine learning | biology | biological datasets | proteomics | sequence analysis | sequence alignment | genome assembly | network motifs | network evolution | graph algorithms | phylogenetics | python | probability | statistics | entropy | information

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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HST.508 Quantitative Genomics (MIT)

Description

This course provides a foundation in the following four areas: evolutionary and population genetics; comparative genomics; structural genomics and proteomics; and functional genomics and regulation.

Subjects

genomics | quantitative genomics | comparative genomics | genes | genome | SNPs | haplotypes | sequence alignment | protein structure | protein folding | proteomics | structural genomics | functional genomics | networks | systems biology | biological networks | RNA | DNA | gene expression | evolutionary genetics | population genetics

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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6.096 Algorithms for Computational Biology (MIT)

Description

This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks.

Subjects

biological sequence analysis | gene finding | motif discovery | RNA folding | global and local sequence alignment | genome assembly | comparative genomics | genome duplication | genome rearrangements | evolutionary theory | gene expression | clustering algorithms | scale-free networks | machine learning applications

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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6.881 Computational Personal Genomics: Making Sense of Complete Genomes (MIT)

Description

With the growing availability and lowering costs of genotyping and personal genome sequencing, the focus has shifted from the ability to obtain the sequence to the ability to make sense of the resulting information. This course is aimed at exploring the computational challenges associated with interpreting how sequence differences between individuals lead to phenotypic differences in gene expression, disease predisposition, or response to treatment.

Subjects

Genomes | Networks | Evolution | computational biology | genomics | comparative genomics | epigenomics | functional genomics | motifs | phylogenomics | personal genomics | algorithms | machine learning | biology | biological datasets | proteomics | sequence analysis | sequence alignment | genome assembly | network motifs | network evolution | graph algorithms | phylogenetics | python | probability | statistics | entropy | information

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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